Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations1905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory201.0 KiB
Average record size in memory108.1 B

Variable types

Numeric8
Text1
Categorical1
Boolean28

Alerts

balcony is highly overall correlated with built_in_wardrobes and 3 other fieldsHigh correlation
barbecue_area is highly overall correlated with quality and 1 other fieldsHigh correlation
built_in_wardrobes is highly overall correlated with balcony and 2 other fieldsHigh correlation
central_ac is highly overall correlated with quality and 1 other fieldsHigh correlation
childrens_play_area is highly overall correlated with qualityHigh correlation
childrens_pool is highly overall correlated with lobby_in_building and 5 other fieldsHigh correlation
covered_parking is highly overall correlated with qualityHigh correlation
kitchen_appliances is highly overall correlated with qualityHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
lobby_in_building is highly overall correlated with childrens_pool and 5 other fieldsHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
maid_service is highly overall correlated with childrens_pool and 4 other fieldsHigh correlation
networked is highly overall correlated with childrens_pool and 6 other fieldsHigh correlation
no_of_bathrooms is highly overall correlated with no_of_bedrooms and 2 other fieldsHigh correlation
no_of_bedrooms is highly overall correlated with no_of_bathrooms and 2 other fieldsHigh correlation
price is highly overall correlated with no_of_bathrooms and 3 other fieldsHigh correlation
price_per_sqft is highly overall correlated with latitude and 1 other fieldsHigh correlation
quality is highly overall correlated with balcony and 18 other fieldsHigh correlation
security is highly overall correlated with qualityHigh correlation
shared_gym is highly overall correlated with balcony and 2 other fieldsHigh correlation
shared_pool is highly overall correlated with balcony and 4 other fieldsHigh correlation
shared_spa is highly overall correlated with childrens_pool and 4 other fieldsHigh correlation
size_in_sqft is highly overall correlated with no_of_bathrooms and 2 other fieldsHigh correlation
study is highly overall correlated with networked and 2 other fieldsHigh correlation
vastu_compliant is highly overall correlated with barbecue_area and 7 other fieldsHigh correlation
view_of_landmark is highly overall correlated with qualityHigh correlation
walk_in_closet is highly overall correlated with qualityHigh correlation
childrens_pool is highly imbalanced (58.2%) Imbalance
lobby_in_building is highly imbalanced (51.4%) Imbalance
maid_service is highly imbalanced (58.9%) Imbalance
networked is highly imbalanced (66.5%) Imbalance
private_garden is highly imbalanced (87.7%) Imbalance
private_gym is highly imbalanced (93.4%) Imbalance
private_jacuzzi is highly imbalanced (71.0%) Imbalance
private_pool is highly imbalanced (74.4%) Imbalance
shared_spa is highly imbalanced (52.5%) Imbalance
study is highly imbalanced (52.7%) Imbalance
vastu_compliant is highly imbalanced (73.0%) Imbalance
id has unique values Unique
no_of_bedrooms has 124 (6.5%) zeros Zeros

Reproduction

Analysis started2025-06-08 15:56:38.695263
Analysis finished2025-06-08 15:56:55.173858
Duration16.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct1905
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7573308.2
Minimum5528049
Maximum7706643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:55.309776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5528049
5-th percentile7242928.2
Q17560167
median7631829
Q37670328
95-th percentile7698455.4
Maximum7706643
Range2178594
Interquartile range (IQR)110161

Descriptive statistics

Standard deviation192525.17
Coefficient of variation (CV)0.025421542
Kurtosis22.265488
Mean7573308.2
Median Absolute Deviation (MAD)46341
Skewness-4.0141885
Sum1.4427152 × 1010
Variance3.7065942 × 1010
MonotonicityStrictly increasing
2025-06-08T19:56:55.511651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5528049 1
 
0.1%
7660851 1
 
0.1%
7661781 1
 
0.1%
7661719 1
 
0.1%
7661690 1
 
0.1%
7661664 1
 
0.1%
7661362 1
 
0.1%
7661301 1
 
0.1%
7661246 1
 
0.1%
7661237 1
 
0.1%
Other values (1895) 1895
99.5%
ValueCountFrequency (%)
5528049 1
0.1%
6008529 1
0.1%
6034542 1
0.1%
6326063 1
0.1%
6356778 1
0.1%
6356784 1
0.1%
6356790 1
0.1%
6356797 1
0.1%
6376886 1
0.1%
6406935 1
0.1%
ValueCountFrequency (%)
7706643 1
0.1%
7706591 1
0.1%
7706389 1
0.1%
7706287 1
0.1%
7705450 1
0.1%
7705439 1
0.1%
7705389 1
0.1%
7705293 1
0.1%
7705124 1
0.1%
7705052 1
0.1%
Distinct54
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:55.772491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length24
Mean length15.025197
Min length4

Characters and Unicode

Total characters28623
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowPalm Jumeirah
2nd rowPalm Jumeirah
3rd rowJumeirah Lake Towers
4th rowCulture Village
5th rowPalm Jumeirah
ValueCountFrequency (%)
dubai 785
18.0%
jumeirah 608
13.9%
downtown 302
 
6.9%
marina 288
 
6.6%
village 214
 
4.9%
circle 200
 
4.6%
palm 178
 
4.1%
city 121
 
2.8%
residence 117
 
2.7%
beach 116
 
2.7%
Other values (70) 1443
33.0%
2025-06-08T19:56:56.298165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3097
 
10.8%
i 2742
 
9.6%
2467
 
8.6%
e 2265
 
7.9%
u 1687
 
5.9%
r 1616
 
5.6%
n 1388
 
4.8%
D 1147
 
4.0%
l 1136
 
4.0%
o 1063
 
3.7%
Other values (42) 10015
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3097
 
10.8%
i 2742
 
9.6%
2467
 
8.6%
e 2265
 
7.9%
u 1687
 
5.9%
r 1616
 
5.6%
n 1388
 
4.8%
D 1147
 
4.0%
l 1136
 
4.0%
o 1063
 
3.7%
Other values (42) 10015
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3097
 
10.8%
i 2742
 
9.6%
2467
 
8.6%
e 2265
 
7.9%
u 1687
 
5.9%
r 1616
 
5.6%
n 1388
 
4.8%
D 1147
 
4.0%
l 1136
 
4.0%
o 1063
 
3.7%
Other values (42) 10015
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3097
 
10.8%
i 2742
 
9.6%
2467
 
8.6%
e 2265
 
7.9%
u 1687
 
5.9%
r 1616
 
5.6%
n 1388
 
4.8%
D 1147
 
4.0%
l 1136
 
4.0%
o 1063
 
3.7%
Other values (42) 10015
35.0%

latitude
Real number (ℝ)

High correlation 

Distinct723
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.116538
Minimum24.865992
Maximum25.273623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:56.457067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.865992
5-th percentile25.03335
Q125.072791
median25.0967
Q325.18813
95-th percentile25.209937
Maximum25.273623
Range0.407631
Interquartile range (IQR)0.115339

Descriptive statistics

Standard deviation0.062647107
Coefficient of variation (CV)0.0024942572
Kurtosis-0.95471401
Mean25.116538
Median Absolute Deviation (MAD)0.040973
Skewness0.26315529
Sum47847.005
Variance0.00392466
MonotonicityNot monotonic
2025-06-08T19:56:56.680929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.04866 44
 
2.3%
25.240419 29
 
1.5%
25.197316 22
 
1.2%
25.046296 22
 
1.2%
25.109367 20
 
1.0%
25.051528 18
 
0.9%
25.086376 17
 
0.9%
25.183515 17
 
0.9%
25.048095 17
 
0.9%
25.072569 15
 
0.8%
Other values (713) 1684
88.4%
ValueCountFrequency (%)
24.865992 1
0.1%
24.934402 2
0.1%
24.942372 1
0.1%
24.946417 1
0.1%
24.948068 1
0.1%
24.978409 1
0.1%
24.98987 1
0.1%
24.998576 1
0.1%
24.999981 1
0.1%
25.00292 1
0.1%
ValueCountFrequency (%)
25.273623 1
 
0.1%
25.240419 29
1.5%
25.233787 7
 
0.4%
25.227337 5
 
0.3%
25.227295 4
 
0.2%
25.22724 1
 
0.1%
25.226946 6
 
0.3%
25.226577 7
 
0.4%
25.225739 1
 
0.1%
25.223728 1
 
0.1%

longitude
Real number (ℝ)

High correlation 

Distinct722
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.212338
Minimum55.069311
Maximum55.441623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:56.934776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55.069311
5-th percentile55.131931
Q155.145389
median55.207506
Q355.271797
95-th percentile55.315196
Maximum55.441623
Range0.372312
Interquartile range (IQR)0.126408

Descriptive statistics

Standard deviation0.068794406
Coefficient of variation (CV)0.001245997
Kurtosis-0.66057396
Mean55.212338
Median Absolute Deviation (MAD)0.063496
Skewness0.44432984
Sum105179.5
Variance0.0047326703
MonotonicityNot monotonic
2025-06-08T19:56:57.191614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.20955 44
 
2.3%
55.25277 29
 
1.5%
55.274196 22
 
1.2%
55.200783 21
 
1.1%
55.24798 20
 
1.0%
55.212972 18
 
0.9%
55.14736 17
 
0.9%
55.26667 17
 
0.9%
55.206373 17
 
0.9%
55.126527 15
 
0.8%
Other values (712) 1685
88.5%
ValueCountFrequency (%)
55.069311 1
 
0.1%
55.09212 1
 
0.1%
55.105457 1
 
0.1%
55.109525 4
0.2%
55.110888 1
 
0.1%
55.111349 4
0.2%
55.112014 2
0.1%
55.119736 1
 
0.1%
55.12067 3
0.2%
55.121042 1
 
0.1%
ValueCountFrequency (%)
55.441623 6
0.3%
55.418 1
 
0.1%
55.408966 1
 
0.1%
55.404096 1
 
0.1%
55.402921 1
 
0.1%
55.402315 1
 
0.1%
55.40066 1
 
0.1%
55.398989 1
 
0.1%
55.398644 2
 
0.1%
55.396845 1
 
0.1%

price
Real number (ℝ)

High correlation 

Distinct821
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2085829.9
Minimum220000
Maximum35000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:57.392491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum220000
5-th percentile460577.6
Q1890000
median1400000
Q32200000
95-th percentile4900000
Maximum35000000
Range34780000
Interquartile range (IQR)1310000

Descriptive statistics

Standard deviation2913200
Coefficient of variation (CV)1.3966623
Kurtosis48.856572
Mean2085829.9
Median Absolute Deviation (MAD)600000
Skewness6.1474026
Sum3.9735059 × 109
Variance8.486734 × 1012
MonotonicityNot monotonic
2025-06-08T19:56:57.747272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300000 31
 
1.6%
1650000 29
 
1.5%
1200000 28
 
1.5%
1100000 26
 
1.4%
1700000 24
 
1.3%
1400000 24
 
1.3%
2100000 24
 
1.3%
1500000 24
 
1.3%
1250000 23
 
1.2%
2000000 21
 
1.1%
Other values (811) 1651
86.7%
ValueCountFrequency (%)
220000 1
0.1%
230000 2
0.1%
245000 1
0.1%
250000 1
0.1%
260000 1
0.1%
270000 1
0.1%
275000 1
0.1%
280000 1
0.1%
290000 1
0.1%
300000 1
0.1%
ValueCountFrequency (%)
35000000 1
0.1%
34340000 1
0.1%
34314000 1
0.1%
31440000 1
0.1%
30950000 1
0.1%
27500000 1
0.1%
22640000 1
0.1%
22260000 1
0.1%
21990000 1
0.1%
21870000 1
0.1%

size_in_sqft
Real number (ℝ)

High correlation 

Distinct1121
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1417.0504
Minimum294
Maximum9576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:58.006114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum294
5-th percentile504.6
Q1840
median1271
Q31703
95-th percentile2936
Maximum9576
Range9282
Interquartile range (IQR)863

Descriptive statistics

Standard deviation891.48764
Coefficient of variation (CV)0.62911499
Kurtosis14.276482
Mean1417.0504
Median Absolute Deviation (MAD)431
Skewness2.9075748
Sum2699481
Variance794750.21
MonotonicityNot monotonic
2025-06-08T19:56:58.238970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 10
 
0.5%
1300 10
 
0.5%
851 9
 
0.5%
1133 8
 
0.4%
758 7
 
0.4%
760 7
 
0.4%
840 7
 
0.4%
1450 7
 
0.4%
915 7
 
0.4%
895 7
 
0.4%
Other values (1111) 1826
95.9%
ValueCountFrequency (%)
294 1
0.1%
296 1
0.1%
306 1
0.1%
314 2
0.1%
321 2
0.1%
325 1
0.1%
330 1
0.1%
339 1
0.1%
353 2
0.1%
356 1
0.1%
ValueCountFrequency (%)
9576 1
0.1%
8722 1
0.1%
7922 1
0.1%
7346 1
0.1%
7344 1
0.1%
6735 1
0.1%
6542 1
0.1%
6252 1
0.1%
6246 1
0.1%
6160 1
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct1784
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1327.2438
Minimum361.87
Maximum4805.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:58.437846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum361.87
5-th percentile604.9
Q1870.92
median1169.59
Q31622.5
95-th percentile2553.816
Maximum4805.87
Range4444
Interquartile range (IQR)751.58

Descriptive statistics

Standard deviation668.47356
Coefficient of variation (CV)0.50365545
Kurtosis4.7975902
Mean1327.2438
Median Absolute Deviation (MAD)352.4
Skewness1.842166
Sum2528399.4
Variance446856.9
MonotonicityNot monotonic
2025-06-08T19:56:58.649719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
738.32 6
 
0.3%
848.04 6
 
0.3%
833.33 6
 
0.3%
1000 4
 
0.2%
755.87 4
 
0.2%
2000 3
 
0.2%
966 3
 
0.2%
900 3
 
0.2%
1200 3
 
0.2%
3458.09 3
 
0.2%
Other values (1774) 1864
97.8%
ValueCountFrequency (%)
361.87 1
0.1%
373.7 1
0.1%
375.68 1
0.1%
389.21 1
0.1%
403.55 1
0.1%
407.24 1
0.1%
409.04 1
0.1%
410.5 1
0.1%
410.54 1
0.1%
410.63 1
0.1%
ValueCountFrequency (%)
4805.87 1
0.1%
4764.5 1
0.1%
4561 1
0.1%
4556.13 1
0.1%
4521.63 1
0.1%
4502.47 1
0.1%
4502.16 1
0.1%
4454.14 1
0.1%
4419.19 1
0.1%
4240.73 1
0.1%

no_of_bedrooms
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7931759
Minimum0
Maximum5
Zeros124
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:58.829606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94948883
Coefficient of variation (CV)0.52950124
Kurtosis-0.12502089
Mean1.7931759
Median Absolute Deviation (MAD)1
Skewness0.29721922
Sum3416
Variance0.90152904
MonotonicityNot monotonic
2025-06-08T19:56:58.989507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 729
38.3%
1 639
33.5%
3 338
17.7%
0 124
 
6.5%
4 70
 
3.7%
5 5
 
0.3%
ValueCountFrequency (%)
0 124
 
6.5%
1 639
33.5%
2 729
38.3%
3 338
17.7%
4 70
 
3.7%
5 5
 
0.3%
ValueCountFrequency (%)
5 5
 
0.3%
4 70
 
3.7%
3 338
17.7%
2 729
38.3%
1 639
33.5%
0 124
 
6.5%

no_of_bathrooms
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5128609
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2025-06-08T19:56:59.138418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0631171
Coefficient of variation (CV)0.4230704
Kurtosis0.050821146
Mean2.5128609
Median Absolute Deviation (MAD)1
Skewness0.56756717
Sum4787
Variance1.1302179
MonotonicityNot monotonic
2025-06-08T19:56:59.272333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 723
38.0%
3 553
29.0%
1 311
16.3%
4 230
 
12.1%
5 77
 
4.0%
6 11
 
0.6%
ValueCountFrequency (%)
1 311
16.3%
2 723
38.0%
3 553
29.0%
4 230
 
12.1%
5 77
 
4.0%
6 11
 
0.6%
ValueCountFrequency (%)
6 11
 
0.6%
5 77
 
4.0%
4 230
 
12.1%
3 553
29.0%
2 723
38.0%
1 311
16.3%

quality
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Medium
1146 
Low
544 
High
134 
Ultra
 
81

Length

Max length6
Median length6
Mean length4.960105
Min length3

Characters and Unicode

Total characters9449
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 1146
60.2%
Low 544
28.6%
High 134
 
7.0%
Ultra 81
 
4.3%

Length

2025-06-08T19:56:59.423241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T19:56:59.551163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 1146
60.2%
low 544
28.6%
high 134
 
7.0%
ultra 81
 
4.3%

Most occurring characters

ValueCountFrequency (%)
i 1280
13.5%
M 1146
12.1%
d 1146
12.1%
u 1146
12.1%
m 1146
12.1%
e 1146
12.1%
o 544
5.8%
w 544
5.8%
L 544
5.8%
H 134
 
1.4%
Other values (7) 673
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1280
13.5%
M 1146
12.1%
d 1146
12.1%
u 1146
12.1%
m 1146
12.1%
e 1146
12.1%
o 544
5.8%
w 544
5.8%
L 544
5.8%
H 134
 
1.4%
Other values (7) 673
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1280
13.5%
M 1146
12.1%
d 1146
12.1%
u 1146
12.1%
m 1146
12.1%
e 1146
12.1%
o 544
5.8%
w 544
5.8%
L 544
5.8%
H 134
 
1.4%
Other values (7) 673
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1280
13.5%
M 1146
12.1%
d 1146
12.1%
u 1146
12.1%
m 1146
12.1%
e 1146
12.1%
o 544
5.8%
w 544
5.8%
L 544
5.8%
H 134
 
1.4%
Other values (7) 673
7.1%

maid_room
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1630 
True
275 
ValueCountFrequency (%)
False 1630
85.6%
True 275
 
14.4%
2025-06-08T19:56:59.650104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1222 
False
683 
ValueCountFrequency (%)
True 1222
64.1%
False 683
35.9%
2025-06-08T19:56:59.736051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

balcony
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1371 
False
534 
ValueCountFrequency (%)
True 1371
72.0%
False 534
 
28.0%
2025-06-08T19:56:59.830992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

barbecue_area
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1636 
True
269 
ValueCountFrequency (%)
False 1636
85.9%
True 269
 
14.1%
2025-06-08T19:56:59.925930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

built_in_wardrobes
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1361 
False
544 
ValueCountFrequency (%)
True 1361
71.4%
False 544
 
28.6%
2025-06-08T19:56:59.998885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

central_ac
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1301 
False
604 
ValueCountFrequency (%)
True 1301
68.3%
False 604
31.7%
2025-06-08T19:57:00.081833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

childrens_play_area
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1266 
True
639 
ValueCountFrequency (%)
False 1266
66.5%
True 639
33.5%
2025-06-08T19:57:00.154789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

childrens_pool
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1744 
True
 
161
ValueCountFrequency (%)
False 1744
91.5%
True 161
 
8.5%
2025-06-08T19:57:00.233741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

concierge
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1226 
True
679 
ValueCountFrequency (%)
False 1226
64.4%
True 679
35.6%
2025-06-08T19:57:00.307696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

covered_parking
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1178 
False
727 
ValueCountFrequency (%)
True 1178
61.8%
False 727
38.2%
2025-06-08T19:57:00.383647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

kitchen_appliances
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1214 
True
691 
ValueCountFrequency (%)
False 1214
63.7%
True 691
36.3%
2025-06-08T19:57:00.454606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

lobby_in_building
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1704 
True
201 
ValueCountFrequency (%)
False 1704
89.4%
True 201
 
10.6%
2025-06-08T19:57:00.520566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

maid_service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1748 
True
 
157
ValueCountFrequency (%)
False 1748
91.8%
True 157
 
8.2%
2025-06-08T19:57:00.586525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

networked
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1787 
True
 
118
ValueCountFrequency (%)
False 1787
93.8%
True 118
 
6.2%
2025-06-08T19:57:00.651485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1338 
True
567 
ValueCountFrequency (%)
False 1338
70.2%
True 567
29.8%
2025-06-08T19:57:00.717443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

private_garden
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1873 
True
 
32
ValueCountFrequency (%)
False 1873
98.3%
True 32
 
1.7%
2025-06-08T19:57:00.786400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

private_gym
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1890 
True
 
15
ValueCountFrequency (%)
False 1890
99.2%
True 15
 
0.8%
2025-06-08T19:57:00.849363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

private_jacuzzi
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1808 
True
 
97
ValueCountFrequency (%)
False 1808
94.9%
True 97
 
5.1%
2025-06-08T19:57:00.916320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

private_pool
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1823 
True
 
82
ValueCountFrequency (%)
False 1823
95.7%
True 82
 
4.3%
2025-06-08T19:57:00.986279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

security
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1234 
True
671 
ValueCountFrequency (%)
False 1234
64.8%
True 671
35.2%
2025-06-08T19:57:01.104205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

shared_gym
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1258 
False
647 
ValueCountFrequency (%)
True 1258
66.0%
False 647
34.0%
2025-06-08T19:57:01.343058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

shared_pool
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
True
1404 
False
501 
ValueCountFrequency (%)
True 1404
73.7%
False 501
 
26.3%
2025-06-08T19:57:01.463983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

shared_spa
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1711 
True
194 
ValueCountFrequency (%)
False 1711
89.8%
True 194
 
10.2%
2025-06-08T19:57:01.573916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

study
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1712 
True
193 
ValueCountFrequency (%)
False 1712
89.9%
True 193
 
10.1%
2025-06-08T19:57:01.657862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

vastu_compliant
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1817 
True
 
88
ValueCountFrequency (%)
False 1817
95.4%
True 88
 
4.6%
2025-06-08T19:57:01.723823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

view_of_landmark
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1508 
True
397 
ValueCountFrequency (%)
False 1508
79.2%
True 397
 
20.8%
2025-06-08T19:57:01.790782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1251 
True
654 
ValueCountFrequency (%)
False 1251
65.7%
True 654
34.3%
2025-06-08T19:57:01.855742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

walk_in_closet
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
False
1615 
True
290 
ValueCountFrequency (%)
False 1615
84.8%
True 290
 
15.2%
2025-06-08T19:57:01.922700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-06-08T19:56:52.797323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:43.694932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.933171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:46.335303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.638503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.150571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.396801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.585071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.951229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:43.835845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.127051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:46.482214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.782412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.299479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.532720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.721987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:53.146109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.019733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.305940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:46.652110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.976293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.460380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.685624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.882887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:53.308010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.199619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.473836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:46.812010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:48.232137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.615283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.837531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.074769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:53.457917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.351526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.641732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.000893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:48.507966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.768189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.016420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.246661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:53.600829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.502437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.800635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.184782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:48.655874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:49.916097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.182318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.386575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:53.739743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.643348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:45.980525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.338686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:48.800785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.086992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.315235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.525490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:54.029564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:44.779263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:46.176401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:47.485595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:48.969682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:50.252890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:51.449152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-08T19:56:52.661408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-08T19:57:02.093597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
balconybarbecue_areabuilt_in_wardrobescentral_acchildrens_play_areachildrens_poolconciergecovered_parkingidkitchen_applianceslatitudelobby_in_buildinglongitudemaid_roommaid_servicenetworkedno_of_bathroomsno_of_bedroomspets_allowedpriceprice_per_sqftprivate_gardenprivate_gymprivate_jacuzziprivate_poolqualitysecurityshared_gymshared_poolshared_spasize_in_sqftstudyunfurnishedvastu_compliantview_of_landmarkview_of_waterwalk_in_closet
balcony1.0000.2100.5020.4950.2950.1690.2160.4370.0760.3110.0860.1770.1210.1900.1620.1320.0000.0000.2790.0380.0670.0000.0000.0520.0620.6520.2890.5080.5200.1130.0040.1440.3450.1270.1790.2670.193
barbecue_area0.2101.0000.2170.2380.4410.4700.2650.1760.0760.2650.2140.4320.2750.1990.3300.4550.0270.0140.2680.0000.1210.0000.0000.1890.0300.5780.0650.2280.2050.3540.0320.3700.0480.5030.1820.2820.215
built_in_wardrobes0.5020.2171.0000.4920.3540.1340.2010.4420.0240.3200.0350.1450.0990.1370.1300.1440.0000.0420.2390.0080.0730.0350.0050.0990.0000.6260.2160.4480.5290.1440.0000.1740.3350.1290.1700.2980.174
central_ac0.4950.2380.4921.0000.3580.1920.2170.4240.0670.2590.1400.2160.1830.1510.1680.1670.0230.0790.2560.0540.0580.0540.0170.1010.0600.6440.3380.4720.5340.1630.0410.1660.2860.1460.1660.2390.169
childrens_play_area0.2950.4410.3540.3581.0000.3650.2000.1990.1020.2670.1780.3360.2490.2050.2610.3260.0000.0000.2820.0130.1210.0000.0220.1550.0850.5100.0910.2800.3040.2280.0220.2520.0250.2960.1190.2550.176
childrens_pool0.1690.4700.1340.1920.3651.0000.2870.2160.0270.2310.2980.8140.3560.2640.5300.7090.0620.0000.1950.0630.1570.0000.0000.0430.0340.7830.3980.1870.1610.5490.0330.4260.0580.6750.4730.2580.388
concierge0.2160.2650.2010.2170.2000.2871.0000.2740.0990.2250.0470.3060.1700.1370.3080.2470.0510.0670.0970.1050.1430.0000.0000.1070.0740.4320.1480.2310.2430.2540.0620.2490.0980.2560.3150.2770.161
covered_parking0.4370.1760.4420.4240.1990.2160.2741.0000.0710.3050.1020.2320.1790.1660.2210.1940.0000.0620.2280.0720.0980.0000.0460.0570.0970.5690.3130.3730.4520.2040.0360.1750.2600.1690.2730.2310.225
id0.0760.0760.0240.0670.1020.0270.0990.0711.0000.035-0.1100.028-0.0000.0500.0750.139-0.035-0.0050.060-0.095-0.1180.0000.0000.0410.0000.0260.0780.0250.0320.094-0.0280.0660.0330.0520.0780.0760.056
kitchen_appliances0.3110.2650.3200.2590.2670.2310.2250.3050.0351.0000.1300.2110.1750.1870.2390.2330.0570.0360.2570.0000.0000.0450.0110.1340.0000.5010.2090.2760.2770.2300.0620.2250.0330.2200.2210.2870.246
latitude0.0860.2140.0350.1400.1780.2980.0470.102-0.1100.1301.0000.2850.5360.1480.3180.3430.1530.1510.2420.4970.5530.1810.0000.0540.0980.2410.2000.0690.0640.2650.2120.2960.0880.3820.1690.2020.295
lobby_in_building0.1770.4320.1450.2160.3360.8140.3060.2320.0280.2110.2851.0000.3620.2350.5270.7090.0530.0420.1840.0550.1690.0000.0000.0560.0370.7250.4460.1850.1510.5200.0480.3970.0130.6040.4900.2640.418
longitude0.1210.2750.0990.1830.2490.3560.1700.179-0.0000.1750.5360.3621.0000.2300.4250.423-0.035-0.0500.233-0.0440.0490.0000.0310.0950.0450.2950.2470.1220.1080.322-0.0820.3800.1410.4790.2540.2730.354
maid_room0.1900.1990.1370.1510.2050.2640.1370.1660.0500.1870.1480.2350.2301.0000.3020.2560.3460.3240.1870.1540.1370.0240.0510.0000.0350.3940.2420.1590.1470.2240.2790.3440.0340.2970.2090.1650.310
maid_service0.1620.3300.1300.1680.2610.5300.3080.2210.0750.2390.3180.5270.4250.3021.0000.6080.0370.0300.2070.0690.1650.0530.0670.0000.1080.7010.3600.1630.1090.4890.0630.4840.1060.6290.4310.2140.492
networked0.1320.4550.1440.1670.3260.7090.2470.1940.1390.2330.3430.7090.4230.2560.6081.0000.0750.0810.2630.0280.1940.0000.0000.0380.0310.8450.3410.1570.1240.6300.0680.5020.0790.7780.4120.2740.457
no_of_bathrooms0.0000.0270.0000.0230.0000.0620.0510.000-0.0350.0570.1530.053-0.0350.3460.0370.0751.0000.8550.0400.6460.1250.0400.0920.0700.1840.0630.0820.0780.0700.0690.7910.1090.0330.0750.0390.0850.061
no_of_bedrooms0.0000.0140.0420.0790.0000.0000.0670.062-0.0050.0360.1510.042-0.0500.3240.0300.0810.8551.0000.0260.6990.1190.0470.1340.0280.2010.0700.1190.0800.0800.0510.8790.0710.0500.0560.0000.0590.000
pets_allowed0.2790.2680.2390.2560.2820.1950.0970.2280.0600.2570.2420.1840.2330.1870.2070.2630.0400.0261.0000.0840.2350.0670.0000.0330.0030.4180.1640.2340.2460.1950.0550.2430.1510.2030.1430.1380.201
price0.0380.0000.0080.0540.0130.0630.1050.072-0.0950.0000.4970.055-0.0440.1540.0690.0280.6460.6990.0841.0000.6690.0850.1480.1130.3870.0430.0660.0630.0960.0290.7710.0000.0140.0000.0590.0680.050
price_per_sqft0.0670.1210.0730.0580.1210.1570.1430.098-0.1180.0000.5530.1690.0490.1370.1650.1940.1250.1190.2350.6691.0000.0510.1320.0680.2720.1150.1300.0510.0810.1220.0930.1530.0000.1890.0360.1810.163
private_garden0.0000.0000.0350.0540.0000.0000.0000.0000.0000.0450.1810.0000.0000.0240.0530.0000.0400.0470.0670.0850.0511.0000.2420.0880.1820.1110.0380.0000.0000.0000.1170.0820.0450.0000.0000.0000.034
private_gym0.0000.0000.0050.0170.0220.0000.0000.0460.0000.0110.0000.0000.0310.0510.0670.0000.0920.1340.0000.1480.1320.2421.0000.0980.2580.0880.0610.0200.0000.0540.1860.0540.0270.0000.0440.0000.066
private_jacuzzi0.0520.1890.0990.1010.1550.0430.1070.0570.0410.1340.0540.0560.0950.0000.0000.0380.0700.0280.0330.1130.0680.0880.0981.0000.0830.1600.0900.1260.0610.0000.0860.0480.0130.0250.0390.1140.017
private_pool0.0620.0300.0000.0600.0850.0340.0740.0970.0000.0000.0980.0370.0450.0350.1080.0310.1840.2010.0030.3870.2720.1820.2580.0831.0000.1250.0470.0000.0030.0660.3130.0000.0770.0000.0410.0260.068
quality0.6520.5780.6260.6440.5100.7830.4320.5690.0260.5010.2410.7250.2950.3940.7010.8450.0630.0700.4180.0430.1150.1110.0880.1600.1251.0000.5420.6740.6610.6770.0400.6420.3500.9460.5640.4540.580
security0.2890.0650.2160.3380.0910.3980.1480.3130.0780.2090.2000.4460.2470.2420.3600.3410.0820.1190.1640.0660.1300.0380.0610.0900.0470.5421.0000.3760.2860.4180.0610.2670.0630.2900.4780.1580.456
shared_gym0.5080.2280.4480.4720.2800.1870.2310.3730.0250.2760.0690.1850.1220.1590.1630.1570.0780.0800.2340.0630.0510.0000.0200.1260.0000.6740.3761.0000.7030.2130.0780.1820.2550.1540.2650.2850.190
shared_pool0.5200.2050.5290.5340.3040.1610.2430.4520.0320.2770.0640.1510.1080.1470.1090.1240.0700.0800.2460.0960.0810.0000.0000.0610.0030.6610.2860.7031.0000.1660.1110.1410.3670.1270.2390.2790.143
shared_spa0.1130.3540.1440.1630.2280.5490.2540.2040.0940.2300.2650.5200.3220.2240.4890.6300.0690.0510.1950.0290.1220.0000.0540.0000.0660.6770.4180.2130.1661.0000.0700.4070.0650.5910.4610.2770.449
size_in_sqft0.0040.0320.0000.0410.0220.0330.0620.036-0.0280.0620.2120.048-0.0820.2790.0630.0680.7910.8790.0550.7710.0930.1170.1860.0860.3130.0400.0610.0780.1110.0701.0000.0230.0000.0500.0560.0590.040
study0.1440.3700.1740.1660.2520.4260.2490.1750.0660.2250.2960.3970.3800.3440.4840.5020.1090.0710.2430.0000.1530.0820.0540.0480.0000.6420.2670.1820.1410.4070.0231.0000.0250.5680.2960.2200.407
unfurnished0.3450.0480.3350.2860.0250.0580.0980.2600.0330.0330.0880.0130.1410.0340.1060.0790.0330.0500.1510.0140.0000.0450.0270.0130.0770.3500.0630.2550.3670.0650.0000.0251.0000.0910.0330.0990.027
vastu_compliant0.1270.5030.1290.1460.2960.6750.2560.1690.0520.2200.3820.6040.4790.2970.6290.7780.0750.0560.2030.0000.1890.0000.0000.0250.0000.9460.2900.1540.1270.5910.0500.5680.0911.0000.4010.2640.495
view_of_landmark0.1790.1820.1700.1660.1190.4730.3150.2730.0780.2210.1690.4900.2540.2090.4310.4120.0390.0000.1430.0590.0360.0000.0440.0390.0410.5640.4780.2650.2390.4610.0560.2960.0330.4011.0000.2940.446
view_of_water0.2670.2820.2980.2390.2550.2580.2770.2310.0760.2870.2020.2640.2730.1650.2140.2740.0850.0590.1380.0680.1810.0000.0000.1140.0260.4540.1580.2850.2790.2770.0590.2200.0990.2640.2941.0000.192
walk_in_closet0.1930.2150.1740.1690.1760.3880.1610.2250.0560.2460.2950.4180.3540.3100.4920.4570.0610.0000.2010.0500.1630.0340.0660.0170.0680.5800.4560.1900.1430.4490.0400.4070.0270.4950.4460.1921.000

Missing values

2025-06-08T19:56:54.377347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-08T19:56:54.833067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idneighborhoodlatitudelongitudepricesize_in_sqftprice_per_sqftno_of_bedroomsno_of_bathroomsqualitymaid_roomunfurnishedbalconybarbecue_areabuilt_in_wardrobescentral_acchildrens_play_areachildrens_poolconciergecovered_parkingkitchen_applianceslobby_in_buildingmaid_servicenetworkedpets_allowedprivate_gardenprivate_gymprivate_jacuzziprivate_poolsecurityshared_gymshared_poolshared_spastudyvastu_compliantview_of_landmarkview_of_waterwalk_in_closet
05528049Palm Jumeirah25.11320855.138932270000010792502.3212MediumFalseFalseTrueTrueFalseTrueTrueFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
16008529Palm Jumeirah25.10680955.151201285000015821801.5222MediumFalseFalseTrueFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueFalse
26034542Jumeirah Lake Towers25.06330255.13772811500001951589.4435MediumTrueTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueTrueTrueFalseFalseFalseTrueTrueTrue
36326063Culture Village25.22729555.341761285000020201410.8923LowFalseTrueTrueFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
46356778Palm Jumeirah25.11427555.13976417292005073410.6501MediumFalseFalseFalseFalseTrueTrueFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalseTrueTrueFalse
56356784Palm Jumeirah25.11427555.139764311990010153073.7912MediumFalseFalseFalseFalseTrueTrueFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalseTrueTrueFalse
66356790Palm Jumeirah25.11427555.139764850360020624123.9623HighTrueFalseFalseFalseTrueTrueFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalseTrueTrueTrue
76356797Palm Jumeirah25.11427555.139764311990010153073.7912MediumFalseFalseFalseFalseTrueTrueFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalseTrueTrueFalse
86376886Palm Jumeirah25.10666855.14927521000002186960.6633LowFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
96406935Downtown Dubai25.19493555.282665269000015211768.5723MediumFalseFalseTrueFalseTrueTrueFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueFalse
idneighborhoodlatitudelongitudepricesize_in_sqftprice_per_sqftno_of_bedroomsno_of_bathroomsqualitymaid_roomunfurnishedbalconybarbecue_areabuilt_in_wardrobescentral_acchildrens_play_areachildrens_poolconciergecovered_parkingkitchen_applianceslobby_in_buildingmaid_servicenetworkedpets_allowedprivate_gardenprivate_gymprivate_jacuzziprivate_poolsecurityshared_gymshared_poolshared_spastudyvastu_compliantview_of_landmarkview_of_waterwalk_in_closet
18957705052Dubai Marina25.08124355.14512013500001578855.5124LowFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
18967705124Downtown Dubai25.19648955.2721261804088852533434.4044MediumTrueTrueTrueFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalse
18977705293Al Quoz25.15308055.254242360000600600.0001MediumFalseFalseTrueFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueTrueTrueFalseFalseFalseFalseFalseTrue
18987705389Palm Jumeirah25.10433055.148769270000010762509.2912MediumFalseTrueTrueFalseTrueFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueTrue
18997705439Dubai Sports City25.03747755.221942550000847649.3512MediumFalseTrueTrueFalseFalseTrueFalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueTrueFalseFalseFalseFalseFalseFalse
19007705450Mohammed Bin Rashid City25.17689255.310712150000010871379.9422UltraFalseTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseTrueTrueTrueTrueTrueTrueTrueTrueTrue
19017706287Mohammed Bin Rashid City25.16614555.27668412300007601618.4212MediumFalseFalseTrueFalseTrueTrueTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueTrue
19027706389Dubai Creek Harbour (The Lagoons)25.20650055.345056290000019301502.5935MediumTrueTrueTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
19037706591Jumeirah Village Circle25.07385855.229844675000740912.1612MediumFalseTrueTrueFalseTrueTrueTrueFalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueTrueTrueFalseFalseFalseFalseTrueTrue
19047706643Jumeirah Lake Towers25.07913055.154713760887800951.1112HighFalseFalseTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueTrueTrueFalseFalseFalseTrueFalse